- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Physics Education Research

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Using digital prompts to support physics students’ self-regulated learning

Louis Leblond1,*, Ying Wang2, Jennelle L. Malcos3, and Rayne A. Sperling4,†

  • *Contact author: lul29@psu.edu
  • †Contact author: rsd7@psu.edu
PDF

Phys. Rev. Phys. Educ. Res. 22, 010114 – Published 4 February, 2026

DOI: https://doi.org/10.1103/cftx-hfw5

Abstract

Research indicates that students learn effectively when they actively engage in planning, monitoring, and reflecting on their learning—a process known as self-regulated learning (SRL). This mixed-methods study examined the impact of a semester-long SRL intervention in an undergraduate physics course. In the first year, the instructor taught the course under standard conditions. The following year, after brief training sessions, the same instructor added digital prompts targeting various aspects of SRL to their course assignments. Results showed that students in this intervention group performed better on the final exam after controlling for preexisting self-efficacy, metacognitive awareness, and GPA. However, SRL outcomes did not increase in the intervention condition. Analysis of the prompts revealed that the instructor disproportionally focused on content- and management-related prompts and underutilized motivation, metacognition, and cognitive strategy prompts. Interview with the faculty indicated that the intervention was straightforward to implement, contributed to humanizing the course, and that additional training would have been desirable to help design prompts. Provided appropriate training and support structures are available, these findings show that integrating SRL strategies through digital prompts can be a practical and effective approach for supporting instructors in fostering students’ SRL.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (68)

  1. B. J. Zimmerman, Attaining self-regulation: A social cognitive perspective, in Handbook of Self-Regulation (Elsevier, New York, 2000), pp. 13–39.
  2. P. H. Winne and A. F. Hadwin, Studying as self-regulated learning, in Metacognition in Educational Theory and Practice (Lawrence Erlbaum Associates, Mahwah, NJ, 1998), pp. 277–304.
  3. B. J. Zimmerman and A. R. Moylan, Self-regulation: Where metacognition and motivation intersect, in Handbook of Metacognition in Education (Routledge, New York, 2009), pp. 299–315.
  4. A. S. Donker, H. De Boer, D. Kostons, C. D. Van Ewijk, and M. P. van der Werf, Effectiveness of learning strategy instruction on academic performance: A meta-analysis, Educ. Res. Rev. 11, 1 (2014).
  5. C. A. Wolters and L. D. Hoops, Self-regulated learning interventions for motivationally disengaged college students, in Self-Regulated Learning Interventions with At-Risk Youth: Enhancing Adaptability, Performance, and Well-Being, edited by T. J. Cleary (American Psychological Association, Washington, DC, 2015), pp. 67–88.
  6. J. Broadbent and W. L. Poon, Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review, Internet Higher Educ. 27, 1 (2015).
  7. B. J. Zimmerman and D. H. Schunk, Self-regulated learning and performance: An introduction and an overview, in Handbook of Self-Regulation of Learning and Performance (Routledge, New York, 2011), pp. 1–12.
  8. N. Achufusi-Aka and F. Offiah, The effect of self-regulated learning on academic achievement of secondary school physics students, Afr. J. Educ. Stud. Math. Sci. 8, 29 (2010).
  9. O. V. Anayo, The relationship between self-regulated learning and students’ learning outcomes in physics, Sapientia Found. J. Educ. Sci. Gender Stud. 5 (2023), https://sfjesgs.com/index.php/SFJESGS/article/view/397.
  10. D. Maldonado and J. Stewart, Adding self-regulated learning instruction to an introductory physics class, presented at PER Conf. 2023, 10.1119/perc.2023.pr.Maldonado.
  11. G. W. Rieger, J. McIver, S. Mazabel, and E. W. Burkholder, Supporting students’ self-regulated learning in an introductory physics course, Phys. Teach. 61, 18 (2023).
  12. T. J. Nokes-Malach, K. VanLehn, D. M. Belenky, M. Lichtenstein, and G. Cox, Coordinating principles and examples through analogy and self-explanation, Eur. J. Psychol. Educ. 28, 1237 (2013).
  13. T. Van Gog and N. Rummel, Example-based learning: Integrating cognitive and social-cognitive research perspectives, Educ. Psychol. Rev. 22, 155 (2010).
  14. V. Gjerde, B. Holst, and S. D. Kolstø, Integrating effective learning strategies in basic physics lectures: A thematic analysis, Phys. Rev. Phys. Educ. Res. 17, 010124 (2021).
  15. A. Mason and C. Singh, Do advanced physics students learn from their mistakes without explicit intervention?, Am. J. Phys. 78, 760 (2010).
  16. A. R. Mota, N. Didiş Körhasan, K. Miller, and E. Mazur, Homework as a metacognitive tool in an undergraduate physics course, Phys. Rev. Phys. Educ. Res. 15, 010136 (2019).
  17. E. Sijmkens, M. De Cock, and T. De Laet, Scaffolding students’ use of metacognitive activities using discipline-and topic-specific reflective prompts, Metacognit. Learn. 18, 811 (2023).
  18. G. Taasoobshirazi and J. Farley, Construct validation of the physics metacognition inventory, Int. J. Sci. Educ. 35, 447 (2013).
  19. J. W. Morphew, Changes in metacognitive monitoring accuracy in an introductory physics course, Metacognit. Learn. 16, 89 (2021).
  20. M. Zhang, J. Morphew, and T. Stelzer, Impact of more realistic and earlier practice exams on student metacognition, study behaviors, and exam performance, Phys. Rev. Phys. Educ. Res. 19, 010130 (2023).
  21. Y. Li and C. Singh, Effect of gender, self-efficacy, and interest on perception of the learning environment and outcomes in calculus-based introductory physics courses, Phys. Rev. Phys. Educ. Res. 17, 010143 (2021).
  22. A. Madsen, S. B. McKagan, and E. C. Sayre, How physics instruction impacts students’ beliefs about learning physics: A meta-analysis of 24 studies, Phys. Rev. ST Phys. Educ. Res. 11, 010115 (2015).
  23. J. M. Nissen and J. T. Shemwell, Gender, experience, and self-efficacy in introductory physics, Phys. Rev. Phys. Educ. Res. 12, 020105 (2016).
  24. V. Sawtelle, E. Brewe, and L. H. Kramer, Exploring the relationship between self-efficacy and retention in introductory physics, J. Res. Sci. Teach. 49, 1096 (2012).
  25. G. Taasoobshirazi and M. Carr, A structural equation model of expertise in college physics, J. Educ. Psychol. 101, 630 (2009).
  26. J. L. Nietfeld, L. Cao, and J. W. Osborne, The effect of distributed monitoring exercises and feedback on performance, monitoring accuracy, and self-efficacy, Metacognit. Learn. 1, 159 (2006).
  27. Y. Wang and R. A. Sperling, Characteristics of effective self-regulated learning interventions in mathematics classrooms: A systematic review, Front. Educ. 5, 58 (2020).
  28. E. Yerushalmi, C. Henderson, K. Heller, P. Heller, and V. Kuo, Physics faculty beliefs and values about the teaching and learning of problem solving. I. Mapping the common core, Phys. Rev. ST Phys. Educ. Res. 3, 020109 (2007).
  29. M. Dancy and C. Henderson, Framework for articulating instructional practices and conceptions, Phys. Rev. ST Phys. Educ. Res. 3, 010103 (2007).
  30. C. Henderson, A. Beach, and N. Finkelstein, Facilitating change in undergraduate STEM instructional practices: An analytic review of the literature, J. Res. Sci. Teach. 48, 952 (2011).
  31. B. J. Zimmerman, Becoming a self-regulated learner: An overview, Theory Pract. 41, 64 (2002).
  32. R. S. Jansen, A. Van Leeuwen, J. Janssen, S. Jak, and L. Kester, Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis, Educ. Res. Rev. 28, 100292 (2019).
  33. C. Dignath, G. Buettner, and H.-P. Langfeldt, How can primary school students learn self-regulated learning strategies most effectively?: A meta-analysis on self-regulation training programmes, Educ. Res. Rev. 3, 101 (2008).
  34. M. Theobald, Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis, Contemp. Educ. Psychol. 66, 101976 (2021).
  35. K. A. Ericsson, R. T. Krampe, and C. Tesch-Römer, The role of deliberate practice in the acquisition of expert performance, Psychol. Rev. 100, 363 (1993).
  36. E. T. Torigoe, Emphasizing expert practice with spaced recall, arXiv:1508.00470.
  37. D. J. Jones, K. W. Madison, and C. E. Wieman, Transforming a fourth year modern optics course using a deliberate practice framework, Phys. Rev. ST Phys. Educ. Res. 11, 020108 (2015).
  38. A. Price and C. Wieman, Improved teaching of science and engineering using deliberate practice of problem-solving decisions, Innovative Teach. Learn. 5, 1 (2023).
  39. R. Sperling, Supporting success through educational technology innovations: Prompting students’ self-regulated learning, The Center for Innovation in Online Learning (COIL) and Collaborative Match, The Pennsylvania State University, University Park, PA (2017).
  40. J. C. Tise, R. A. Sperling, J. L. Malcos, and P. Buchanan, Use of a technology-based SRL tool predicts course performance—but response latency moderates the effect, in Proceedings of the American Educational Research Association Annual Meeting, Chicago, IL (2023).
  41. J. L. Malcos, Y. Wang, and R. A. Sperling, The role of student responses to instructor-generated SRL (self-regulated learning) prompts on student performance and self-efficacy, in Proceedings of the American Educational Research Association Annual Meeting, Philadelphia, PA (2024).
  42. D. S. Yeager and G. M. Walton, Social-psychological interventions in education: They’re not magic, Rev. Educ. Res. 81, 267 (2011).
  43. K. R. Binning, N. Kaufmann, E. M. McGreevy, O. Fotuhi, S. Chen, E. Marshman, Z. Y. Kalender, L. Limeri, L. Betancur, and C. Singh, Changing social contexts to foster equity in college science courses: An ecological-belonging intervention, Psychol. Sci. 31, 1059 (2020).
  44. N. J. Cepeda, H. Pashler, E. Vul, J. T. Wixted, and D. Rohrer, Distributed practice in verbal recall tasks: A review and quantitative synthesis, Psychol. Bull. 132, 354 (2006).
  45. Y. Wang, R. A. Sperling, and P. M. Reeves, A validation study of the Self-Efficacy for Strategic Learning in Biology Scales (SESLBS), Int. J. Educ. Res. Open 5, 100302 (2023).
  46. L. W. Anderson, D. R. Krathwohl, P. W. Airasian, K. A. Cruikshank, R. E. Mayer, P. R. Pintrich, J. Raths, and M. C. Wittrock, A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Complete Edition (Longman, New York, 2001).
  47. I. S. Schwartz and D. M. Baer, Social validity assessments: Is current practice state of the art?, J. Appl. Behav. Anal. 24, 189 (1991).
  48. E. Ogilvie and M. T. McCrudden, Evaluating the social validity of the early start Denver model: A convergent mixed methods study, J. Autism Dev. Disord. 47, 2899 (2017).
  49. V. Otero, S. Pollock, and N. Finkelstein, A physics department’s role in preparing physics teachers: The Colorado learning assistant model, Am. J. Phys. 78, 1218 (2010).
  50. A. Bandura, Self-efficacy: Toward a unifying theory of behavioral change, Psychol. Rev. 84, 191 (1977).
  51. G. M. Harrison and L. M. Vallin, Evaluating the metacognitive awareness inventory using empirical factor-structure evidence, Metacognit. Learn. 13, 15 (2018).
  52. G. Schraw and R. S. Dennison, Assessing metacognitive awareness, Contemp. Educ. Psychol. 19, 460 (1994).
  53. R Core Team, r: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).
  54. D. Bates, M. Mächler, B. Bolker, and S. Walker, Fitting linear mixed-effects models using lme4, J. Stat. Softw. 67, 1 (2015).
  55. H. Singmann, B. Bolker, J. Westfall, F. Aust, and M. S. Ben-Shachar, afex: Analysis of Factorial Experiments, R package version 1.4-1 (2024), https://CRAN.R-project.org/package=afex.
  56. R. V. Lenth, emmeans: Estimated Marginal Means, aka Least-Squares Means, R package (2024), https://CRAN.R-project.org/package=emmeans.
  57. I. B. M. Corp, IBM SPSS Statistics for Windows, Version 29.0 (IBM Corp., Armonk, NY, 2023).
  58. L. Leblond, Y. Wang, J. Malcos, and R. Sperling, Using digital prompts to support physics students self-regulated learning, 10.5281/zenodo.15263400.
  59. K. Charmaz, Constructing Grounded Theory, 2nd ed. (Sage, Los Angeles, CA, 2014).
  60. A. Strauss, J. Corbin et al., Basics of Qualitative Research (Sage, Newbury Park, CA, 1990), Vol. 15.
  61. See Supplemental Material at http://link.aps.org/supplemental/10.1103/cftx-hfw5 for a list of digital SRL prompts used in the study with their types, StSRL categories, and quality ratings.
  62. J. A. Rosen, S. R. Porter, and J. Rogers, Understanding student self-reports of academic performance and course-taking behavior, AERA Open 3 (2017).
  63. R. Theobald and S. Freeman, Is it the intervention or the students? Using linear regression to control for student characteristics in undergraduate STEM education research, CBE Life Sci. Educ. 13, 41 (2014).
  64. V. Gjerde, V. Havre Paulsen, B. Holst, and S. D. Kolstø, Problem solving in basic physics: Effective self-explanations based on four elements with support from retrieval practice, Phys. Rev. Phys. Educ. Res. 18, 010136 (2022).
  65. V. Gjerde, M. Marisaldi, K. Oksavik, K. Olafsson, H. Spångberg, and B. Holst, Mandatory retrieval test to incentivize retrieval practice of physics principles, Phys. Rev. Phys. Educ. Res. 21, 010119 (2025).
  66. V. Gjerde, B. Holst, and S. D. Kolstø, Retrieval practice of a hierarchical principle structure in university introductory physics: Making stronger students, Phys. Rev. Phys. Educ. Res. 16, 013103 (2020).
  67. J. L. Malcos, J. C. Tise, and R. A. Sperling, Resource use in a large introductory physiology course, in Proceeding of the Human Anatomy and Physiology Society (HAPS) Annual Conference, Portland, OR (2019).
  68. K.-S. Tang, G. Cooper, and W. Nielsen, Philosophical, legal, ethical, and practical considerations in the emerging use of generative AI in academic journals: Guidelines for research in science education (RISE), Res. Sci. Educ. 54, 797 (2024).

Outline

Information

Phys. Rev. Phys. Educ. Res. 22, 010114– Published 4 February, 2026
  • Received 22 April 2025
  • Accepted 14 January 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation